Carrier-Based Aircraft Landing Control Method Based on Dynamic Data-Driven MPC

Objectives To address the non-Gaussian disturbances caused by the complex carrier airwake during carrier-based aircraft landing, this study introduces dynamic data-driven technology to enhance the precision and robustness of landing control. Methods Targeting the non-Gaussian and strong gust disturbances in the carrier airwake field, a data assimilation algorithm based on an augmented state Particle Filter (PF) is designed, and a gradient descent method is introduced to fine-tune the model parameters online. Based on this data assimilation algorithm, a landing control method for carrier-based aircraft is implemented, which combines Dynamic Data-Driven Model Predictive Control (DDD-MPC) and Nonlinear Dynamic Inversion (NDI). Results Simulation results demonstrate that the proposed method effectively suppresses the severe high-frequency oscillations of the angle of attack induced by strong non-Gaussian gusts. Additionally, attitude adjustments are restricted within a specific range, thereby avoiding the high-frequency oscillations typically generated by traditional MPC controllers during the final approach phase. Conclusions The results verify that the proposed method exhibits excellent performance in resisting carrier airwake disturbances and can guarantee the trajectory tracking accuracy and attitude stability of the carrier-based aircraft.